Introduction

A reworking of a data mining strategy, in which statistical treatment of raw data from liquid chromatography-mass spectrometry (LC-MS) precedes recognition of chromatographic peaks, is presented. In this algorithm the tR-m/z plane of LC-MS data is divided into equal-sized segments of twelve seconds by one m/z unit each, and the total ion currents in corresponding segments as specified by the tR-m/z pair from multiple LC-MS runs are evaluated to generate mean ion currents (μ) and standard deviations (σ). The μ's and σ's of the segments, derived from contrasting classes of LC-MS data set (e.g., resistant-susceptible, case-control, etc.), are used to calculate the Z-factor (screening window coefficient) which is in turn used to rank the segments. Chromatographic peaks are recognized only where the ion currents are shown to differentiate the classes. The result-reporting format enables detection of positive as well as negative correlations between ion intensities and biological traits under study and thus points to the presence of potentially phenotype-discriminating metabolites. Examples of data analyses are presented, in which ions that may distinguish resistant and susceptible species of Aesculus to the leaf-miner Cameraria ohridella were detected.

Publications

  1. Direct and unbiased information recovery from liquid chromatography-mass spectrometry raw data for phenotype-differentiating metabolites based on screening window coefficient of ion currents.
    Cite this
    Kokubun T, D'Costa L, 2013-09-01 - Analytical chemistry

Credits

  1. Tetsuo Kokubun
    Developer

    Jodrell Laboratory, Royal Botanic Gardens, United Kingdom of Great Britain and Northern Ireland

  2. Lilla D'Costa
    Investigator

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Summary
AccessionBT002949
Tool TypeApplication
Category
PlatformsWindows
TechnologiesC++
User InterfaceTerminal Command Line
Download Count0
Submitted ByLilla D'Costa